Pima Indians Diabetes

Pima Indians Diabetes

In this article, we use Kaggle'sPima Indians Diabetes. The Pima Indians are a group of Native Americans living in an area consisting of what is now central and southern Arizona. A variety of statistical methods are used here for predictions.

Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Content

The datasets consist of several medical predictor variables and one target variable, Outcome. Predictor variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Feature Explanations
Pregnancies Number of times pregnant
Glucose Plasma glucose concentration a 2 hours in an oral glucose tolerance test
Blood Pressure Diastolic blood pressure (mm Hg)
Skin Thickness Triceps skinfold thickness (mm)
Insulin 2-Hour serum insulin (mu U/ml)
BMI Body mass index (weight in kg/(height in m)^2)
Diabetes Pedigree Function Diabetes pedigree function
Age Age (years)
Outcome Whether or not a patient has diabetes

Train and Test sets

StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

Modeling: CatBoost Classifier

CatBoost Classifier is based on gradient boosted decision trees. During training, a set of decision trees is built consecutively. Each successive tree is built with reduced loss compared to the previous trees.

The best result for each metric calculated on each validation dataset.


References

  1. Smith, J. W., Everhart, J. E., Dickson, W. C., Knowler, W. C., & Johannes, R. S. (1988). Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proceedings of the Annual Symposium on Computer Application in Medical Care, 261–265.
  2. CatBoost Classifier Documentation